Imperial College London
Alternate

DrVasaCurcin

Faculty of Medicine, School of Public Health

Honorary Lecturer
 
 
 
//

Contact

 

+44 (0)20 7594 0716vasa.curcin Website

 
 
//

Location

 

320Reynolds BuildingCharing Cross Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Wongkoblap:2019:10.1109/EUROCON.2019.8861514,
author = {Wongkoblap, A and Vadillo, MA and Curcin, V},
doi = {10.1109/EUROCON.2019.8861514},
title = {Predicting Social Network Users with Depression from Simulated Temporal Data},
url = {http://dx.doi.org/10.1109/EUROCON.2019.8861514},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Mental health issues are widely accepted as one of the most prominent health challenges in the world, with over 300 million people currently suffering from depression alone. With massive volumes of user-generated data on social networking platforms, researchers are increasingly using machine learning to determine whether this content can be used to detect mental health problems in users. This study aims to investigate whether training a predictive model with multiple instance learning (MIL) via Long Short-Term Memory (LSTM) and gated recurrent unit (GRU) can improve the performance of a predictive model to detect social network users with depression. The power of MIL is to learn from user-level labels to identify post-level labels. By combining every possibility of posts label category, it can generate temporal posting profiles which can then be used to classify users with depression. This study highlights that training a MIL model via LSTM and GRU can improve the accuracy of a MIL model trained with convolutional neural networks.
AU  - Wongkoblap,A
AU  - Vadillo,MA
AU  - Curcin,V
DO  - 10.1109/EUROCON.2019.8861514
PY  - 2019///
TI  - Predicting Social Network Users with Depression from Simulated Temporal Data
UR  - http://dx.doi.org/10.1109/EUROCON.2019.8861514
ER  -
Download